PhD Intern, Machine Learning: MSI, Product Operations Team
Summary
A PhD internship on Apple's Manufacturing and Operations team in Bengaluru, applying machine learning, computer vision, VLMs, and statistics to improve how Apple products are manufactured. Day-to-day involves ML research, prototyping ideas in Python, and presenting analysis that impacts global manufacturing.
Imagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple. This position involves a wide variety of skills, innovation, and is a rare opportunity to be working on groundbreaking, new applications of machine learning, research, and implementation. Ultimately, your work would have a huge impact on billions of users across the globe. You can help inspire change, by using your skills to influence globally recognized products' manufacturing.
The goal of Apple's Manufacturing and Operations team is to take a vision of a product and turn it into a reality. Through the use of statistics, the scientific process, and machine learning, the team recommends and implements solutions to the most challenging problems. We're looking for ML researchers to help us revolutionize how we manufacture Apple's amazing products. Put your skill & passion to work in this highly visible role.
Minimum Qualifications
- Pursuing PhD in Computer Science / related field
- Deep understanding of Machine Learning concepts, Probability and Statistics
- Understanding of Computer Vision & VLMs, with demonstrated skill in training models and applying them to ML application development.
- Ability to prototype research ideas as working code (Python)
- Ability to meaningfully present results of analysis in a clear and impactful manner
Preferred Qualifications
- Familiarity with applying Machine Learning, especially Computer Vision in a Manufacturing / Assembly setting
- Familiarity with multimodal understanding & reasoning, knowledge-graphs & generative video models
- Prior publications in relevant field